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Description
AudioLM is an innovative audio language model designed to create high-quality, coherent speech and piano music by solely learning from raw audio data, eliminating the need for text transcripts or symbolic forms. It organizes audio in a hierarchical manner through two distinct types of discrete tokens: semantic tokens, which are derived from a self-supervised model to capture both phonetic and melodic structures along with broader context, and acoustic tokens, which come from a neural codec to maintain speaker characteristics and intricate waveform details. This model employs a series of three Transformer stages, initiating with the prediction of semantic tokens to establish the overarching structure, followed by the generation of coarse tokens, and culminating in the production of fine acoustic tokens for detailed audio synthesis. Consequently, AudioLM can take just a few seconds of input audio to generate seamless continuations that effectively preserve voice identity and prosody in speech, as well as melody, harmony, and rhythm in music. Remarkably, evaluations by humans indicate that the synthetic continuations produced are almost indistinguishable from actual recordings, demonstrating the technology's impressive authenticity and reliability. This advancement in audio generation underscores the potential for future applications in entertainment and communication, where realistic sound reproduction is paramount.
Description
By incorporating image conditioning techniques alongside a prompt-based editing method, we offer users innovative ways to manipulate 3D synthesis, paving the way for various creative possibilities. Magic3D excels in generating high-quality 3D textured mesh models based on textual prompts. It employs a coarse-to-fine approach that utilizes both low- and high-resolution diffusion priors to effectively learn the 3D representation of the desired content. Moreover, Magic3D produces 3D content with 8 times the resolution supervision compared to DreamFusion, while also operating at twice the speed. Once a rough model is created from an initial text prompt, we can alter elements of the prompt and subsequently fine-tune both the NeRF and 3D mesh models, resulting in an enhanced high-resolution 3D mesh. This versatility not only enhances user creativity but also streamlines the workflow for producing detailed 3D visualizations.
API Access
Has API
API Access
Has API
Integrations
Google Opal
Pricing Details
No price information available.
Free Trial
Free Version
Pricing Details
No price information available.
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
Country
United States
Website
research.google/blog/audiolm-a-language-modeling-approach-to-audio-generation/
Vendor Details
Company Name
Magic3D
Website
research.nvidia.com/labs/dir/magic3d/